The proposed research tests a dynamic systems-based theory, which explains how recovery house residents' recovery-related attitudes, behaviors, and social relationships co-evolve, and how these emergent individual characteristics and house-level social structures subsequently link to individuals' recovery endpoints. The theory adapts concepts from group and social network dynamics, placing them within a broader community mental health framework. It is operationalized and tested by measuring relationships of trust, friendship, and advice/mentoring as dynamic multiplex social networks (Snijders et al., 2012)-multiple, simultaneous interdependent relationships--that exist within each house. These relationships are assumed to co-evolve over time, affecting and affected by recovery-related attitudes and behaviors, and personal networks outside the house. By pooling dynamic relationships across houses, we will apply the Stochastic Actor-Oriented Modeling framework (Snijders et al., 2010) to estimate a set of stochastic, continuous-time difference equations. This model will then be subjected to theoretical analyses designed to suggest possible strategies for improving outcomes (e.g. maintaining residence) for this population. Our proposed study will identify mechanisms through which social environments affect health outcomes, and thereby contribute to reducing unnecessary health care costs by improving the effectiveness of the residential recovery home system in the US and also restructuring and improving other community-based recovery settings. These types of improvements could lead to better client care and treatment outcomes. Our proposed research would provide significant insight on within house structure and dynamics as predictors of an individual's likelihood of maintaining a positive recovery trajectory; it would provide information on the interactions of external recovery behaviors (e.g. AA), external ego-centered networks (scope, composition, dynamics), and within-setting social networks, and it might identify points of failure where the individual reaches a significant likelihood of relapse. In addition, this work should result in an initial framework for the study of network dynamics in recovery homes which should facilitate both the theoretical development and empirical investigation of the broader domain of recovery homes.